Understanding of the concept of redundancy and collinearity in the context of model interpretation of linear regression and variable selection.
The Top 4 Things Attendees will Learn:
Redundancy & Colinearity are not necessarily bad
Distinction between suppression, redundancy and enhancement for real world data
Model interpretation for real world data
Whether or not the valuable is of importance
Leonardo E. Auslender, Senior Statistician/Economist
Leonardo Auslender is a statistician (and economist) with more than 25 years of business experience and SAS expertise. His area of expertise is in the area of Giga-Data Analysis and Methods, and has written papers and given lectures on Variable Selection, Missing Value Imputation, Tree Regression, Support Vector Machines, Market-Basket Analysis, Data Base Marketing, CRM, GDP and (Relative Price) Inflation studies, Expectation Formations, Productivity and Technology effects in the economy. He was a lecturer of Finance and Macroeconomics at Rutgers University.
He presented two seminars on Market Basket Analysis in New York City (Informs and Amcis), a two-day seminar at the NYC Direct Marketing Association on Variable and Feature Selection in November, 2004, on Colinearity and Variable Selection at the December 2005 SCMA meeting in Auburn, Alabama, on Modeling issues at the SAS M2007 Data Mining Conference, and again this year at M2008.
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| Fax: | 212.302.7643 |
| Phone: | 212.790.1500 |
| Mail: | Printable Form DMA Customer Service 1120 Avenue of the Americas New York, NY 10036-6700 (Check payable to DMA) |
For questions/inquiries, call DMA Customer Service at 212.790.1500 or e-mail customerservice@the-dma.org.
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